National and sub-national levels and causes of mortality among 5-19-year-olds in China in 2004-2019: A systematic analysis of evidence from the Disease Surveillance Points System

Background China accounts for 13% of the world’s 5-19-year-olds population. We estimated levels and trends of mortality by sex-age-cause among 5-19-year-olds at national and subnational levels in China annually from 2004 to 2019, to inform strategies for reducing child and adolescent mortality in China and other countries. Methods We used adjusted empirical data on levels and causes of deaths from the China Center for Disease Control and Prevention’s Disease Surveillance Point (DSP) system. We considered underreporting and surveillance sampling design, applied smoothing techniques to produce reliable time trends, and fitted age-specific deaths and population to national estimates produced by international agencies to allow for cross-national comparisons. Results The top leading causes for 54 594 deaths among 5-19-year-olds were neoplasms, road traffic injuries, and drowning. All-cause mortality in 5-19-year-olds has been declining steadily between 2004-2019, with evident yet narrowing geographical and gender disparities. Injury mortalities were one of the fastest declining causes, but widespread disparities were observed across subpopulations. Falling injuries and rising non-communicable diseases had the most pronounced epidemiological transition in the eastern region. Decrease in drowning fractions stalled for 15-19-year-olds in central/western rural areas. Suicide shares sustained or increased for 15-19-year-olds except among females in eastern rural areas. Conclusions China made significant improvements in child and adolescent survival since 2004. However, constant targeted investments are needed to maintain and accelerate progress. A sustainable sample registration system like the DSP is likely essential for supporting such a process.

The system first started with pilot sites of Dongcheng district and Tong county of Beijing in 1978. In 1989, 71 surveillance points in big cities and wealth rural areas from 29 out of 31 provinces volunteered to participate in disease surveillance work. 1 In 1990, to gain national coverage and representativeness, the DSP system selected 145 surveillance points from all 31 provinces, using multi-stage stratified cluster sampling approach. All counties and cities were divided into 13 geographic regions, and then within each region, further stratified into 3 levels of urban and 4 levels of rural areas based on their economic and health status. Then urban communities and rural townships were selected as the surveillance points using probability proportionate to population size sampling (PPS) and systematic sampling in general, though with a few sites replaced considering the local capacity. A total of 58 urban points and 87 rural points were selected included in the DSP system, covering 10 million population. Since then, DSP has been generating estimates on levels and causes of mortality at national level and the six-region-residency-strata level (namely west rural, west urban, central rural, central urban, east rural, east urban). 2 In 2004 and 2005, the DSP system were expanded again to adapt to the rapidly changing mortality rate and regain representativeness. The selection of new surveillance points followed a similar multi-stage stratified cluster sampling approach as the previous expansion. All rural counties and urban cities were divided into first-level strata based on region (east, central, west). Then within each first-level stratum, counties/cities were further stratified into 9 second-level strata by tertiles of economic status (GDP for rural counties and urbanization for urban cities) and tertiles of population size. Existing DSP sites were given priority to remain in the system, and new sites were purposively selected considering local capacity and ge 3 161 points (63 urban points and 98 rural points) were included in DSP covering 73 million population. Evaluation of the system showed decent general representativeness at national, regional, and urban/rural level as measured by a few indicators including crude birth and death rates and population composition. 3 Level  Category  code  Cause name  ICD-10  ICD-9   1  AD1   Communicable, maternal,  perinatal and nutritional  conditions   A00-B99, D50-D53, D64.9, E00-E02,  E40-E46, E50-E68, G00, G03-G04, H65-H66, J00-J22, N70-N73, O00-O99, P00-P96 (minus P23,  Apart from the double-checking and correction during the routine data reporting in local and central CDC, DSP have been conducting retrospective quality assessment and independent underreporting survey every 3 years since 2009 in all DSP sites, to review the completeness of reported deaths and causes of death for the 3 years prior to the survey. Townships and districts were selected following certain criteria, and all households in the selected areas were included in the household survey. All deaths occurred in 3 years prior to the survey year were identified and recorded by DSP staffs from multiple data sources: household interviews, local health facilities, Family Planning Offices, Maternal and Child Health Offices, Civil Affairs Departments, and Public Security Bureaus etc.. Cases not originally captured in routine DSP reporting were defined as missed cases, and causes of death and demographic information were collected for missed cases during the household interviews. Data were then electronically reported via the underreporting survey system and underlying causes were ICD-coded by local CDC.

Underreporting adjustment
The underreporting rates (URR) were calculated at provincial level annually for year 2006-2017 using the equation below.

URR = # missed cases newly identified in the underreporting survey # all cases identified in the underreporting survey
No clear evidence showed that the data completeness and overall quality varied across causes, thus same URRs were applied to adjust all-cause and cause-specific number of deaths.

Sampling probability calculation and data adjustment
We calculated the stratum-specific weights used to adjust the population and get subnational and national representative estimates for number of deaths by cause. Likelihood for being sampled for people from 2 nd -level stratum j (Likelihood j) for each age and gender group could be calculated using: Where i denotes the i th surveillance point in stratum j, mj is the total number of surveillance points in stratum j, i ∈ (1,…,mj).
The total number of deaths within each stratum for each age and gender group could be calculated using: Where ATNDj is adjusted total number of deaths for stratum j, TNDi is the total number of deaths for each surveillance point after adjusting for under-reporting.

Death envelope and crisis
All-cause death envelope is equivalent to all-cause number of death estimates, and is used interchangeably in this manuscript. It's usually used when fitting the cause-specific estimates "within" the all-cause estimates' boundaries, so the number from single causes would add up to match the all-cause totals. "Crisis-free" envelop would thus mean the total number of all causes except crisis.
Natural disasters and collective violence that severely impacted the population on a large scale are considered crisis in WHO-IGME estimates. The identification of crisis was case-by-case. 2008 Sichuan earthquake was identified as the only national natural disaster crisis during the period of 2004-2019 in China. Death estimates due to crisis are usually hard to capture, especially for countries relying on population-based surveys or sample registration system, where national estimates are inferred from sample of a small portion of the population. Therefore, crisis estimates directly from non-vital-registration data sources need to be used with cautious.

Subnational estimates for 2008 Sichuan earthquake
Given that DSP wasn't designed to fully cover all urban and rural areas that was affected by earthquake, and considering the challenges in cause of death data collection and verbal autopsy survey post-disaster via surveillance system, we derived natural disaster estimates for four provinces affected by this crisis -Sichuan, Shaanxi, Gansu and Chongqing, from IGME age-specific crisis estimates, for year 2008. IGME total deaths exceeds the non-crisis deaths envelope were identified as national age-specific crisis death for year 2008. Then these deaths were redistributed to Sichuan, Shaanxi, Gansu and Chongqing provinces proportional to total deaths reported in latest available government reports and mainstream news in these provinces. [5][6][7][8][9] Yunnan, Henan, Hubei, Guizhou and Hunan had reported deaths due to earthquakes, but was either not in age groups of interest, [10][11][12] or was thus were not included in the crisis estimation. Then for urban and rural split of the deaths, we imputed percentage of urban earthquake deaths from sub-regional reporting when available, 6 otherwise used percentage of urban population instead. For Sichuan, we treated earthquake deaths in cities as urban deaths. 5 For Shaanxi, deaths in cities with top 5 GDP per capita in province as urban deaths. 6 For Chongqing, we calculated the proportion based on deaths with location information available. 8 For Gansu, we used percentage of urban population for 2008. 13 Then we used the 5-year-moving-average smoothed gender splits to further breakdown the estimates by gender.

Annual Rate of Reduction (ARR)
The annual rate of reduction (ARR) was calculated by: Last  Figure S1. Provincial trend in all-cause mortality estimates in 2004-2019 Figure S2. ARR for all cause mortality by province Figure S3. Provincial level ARR heatmaps Figure S4. Strata trends of cause of deaths (COD) by age and gender in 2004-2019 Figure S5. Province trends of cause of deaths (COD) in 2004-2019  Table S3. Region-residency strata level cause-specific death, fraction and mortality estimates, 2019 Table S4. National and subnational annual rate of reduction (ARR) by age and sex Note: Estimates are dashed-out if number of death is less than 25. Table S4. National and subnational annual rate of reduction (ARR) by age and sex